I'm trying to implement a perceptron in python using numpy, when using the notation z=XW+b everything is fine. While studying ML I do see though that z=WX+b is also common, especially when talking about neural networks. The problem is that the dimensions of the matrices don't add up, I tried following some answers on the web but the output doesn't have the right dimensions. I tried also asking chatgpt but it only implemented the code following the z=XW+b notation. This is the code I used for z=XW+b: import numpy as np n_inpts = 10 in_feats = 5 n_hidden = 8 out_feats = 1 X = np.random.randn(n_inpts,in_feats) W_x = np.random.randn(in_feats, n_hidden) bias_h = np.random.randn(1, n_hidden) H = np.dot(X,W_x) + bias_h #H is nxh relu = lambda x: max(0, x) v_relu = np.vectorize(relu) H = v_relu(H) W_h = np.random.randn(n_hidden, out_feats) bias_o = np.random.randn(1, out_feats) output = np.dot(H, W_h) + bias_o Can anybody give me an implementation that gives the same result while using z=WX+b? Every single implementation I found follows the z=XW+b notation. I guess it comes down to how you specify the X and W matrices but as of now I have had no luck finding a solution to my question Continue reading...
